Nvidia Accelerator Configuration for Scale

The GPU Silo Crisis

Organizations investing in GPU infrastructure often face the "GPU Silo" problem where expensive GPU hardware remains underutilized due to complex manual configuration, dependency management, and operational overhead. Red Hat AI and the NVIDIA GPU Operator addresses this challenge by automating the deployment and lifecycle management of all necessary GPU software components.

Goal

To equip architects, engineers, and administrators with the knowledge and skills required to configure, manage, and monitor NVIDIA GPU Accelerators on OpenShift. This course serves as the foundational hardware enablement layer for a Models-as-a-Service (MaaS) architecture, ensuring teams can utilize and support these components as part of a revenue-generating services business.

Objectives

On completing this course, you should be able to:

  • Understand and install foundational operators including the NVIDIA GPU Operator stack

  • Configure Multi-Instance GPU (MIG) for maximizing GPU ROI in MaaS deployments

  • Monitor GPU telemetry and health using Grafana and OpenShift observability tools

Audience

  • Platform Engineers: Responsible for designing and deploying model serving environments and enabling MaaS

  • DevOps/SREs: Focused on observability, operational scale, tracking consumption metrics, and cost allocation

Prerequisites

This course assumes that you have the following experience:

  • Familiarity with Kubernetes and Red Hat OpenShift Container Platform

  • Basic understanding of machine learning concepts and Red Hat OpenShift AI

  • Basic networking, routing, and understanding of Kubernetes operators

  • Familiarity with REST APIs, Prometheus metrics, and RBAC within OpenShift